SpaceX has completed the largest initial public offering in history, raising $75 billion. The listing priced 555.6 million shares at $135 each, granting the company a mammoth market valuation of $1.77 trillion.
Source: SpaceX, the Verge
And even if Starlink powers today’s revenue, the future upside is clearly pegged to artificial intelligence, according to SpaceX itself.
Gary Kim was cited as a global "Power Mobile Influencer" by Forbes, ranked second in the world for coverage of the mobile business, and as a "top 10" telecom analyst. He is a member of Mensa, the international organization for people with IQs in the top two percent.
World Cup long-term economic effects will be negligible, economists at Goldman Sachs say. That might seem unlikely, given the 2026 FIFA World Cup featuring 48 teams and 104 matches across the United States, Canada and Mexico.
After Goldman Sachs International economists Kevin Daly and Mambuna Njie studied gross domestic product data covering every World Cup since 1982, they find hosting produces a marginally positive but statistically insignificant effect on real output, with long-run impact that is effectively zero.
FIFA and the World Trade Organization disagree. A joint study they published in April 2025, developed by consultancy OpenEconomics, projects a $17.2 billion contribution to U.S. GDP, $30.5 billion in gross output and approximately 185,000 full-time equivalent jobs for the host country alone.
Across all three host countries, the combined GDP estimate reaches $40.9 billion, the report argues.
Different methodologies help explain the differences.
Beer, merchandise and apparel purchased in their own markets does not register in U.S., Canadian or Mexican GDP.
Domestic spending on World Cup-related goods and services may simply be redirected from other consumption categories rather than representing new activity.
There is short-term lift, but no lasting contribution.
Leakage effects also are real: profits from international licensing, sponsorship and supply chains accrue outside the host country’s GDP.
On the other hand, it stands to reason that several industries should benefit, including:
European and US consumer staples (brewing companies including AB InBev, Molson Coors, Constellation Brands, Heineken and Carlsberg)
European consumer discretionary, primarily sportswear (the ones we know: adidas, PUMA)
U.S. retail and softlines (Academy Sports + Outdoors, Dick’s Sporting Goods, Nike)
U.S. lodging and leisure (Hyatt, Marriott, Hilton, Airbnb)
U.S. airlines.
There will be significant industrial impact in those segments of the market, to be sure. Concentrated, time limited but real.
Wider and long-term benefits will likely be negligible, if measurable at all.
In many ways, the impact is similar to that supposedly created by municipally-financed sports stadia.
The claim that government-financed sports stadia act as engines for economic growth is widely contested within the field of economics.
While proponents often cite job creation, increased tax revenues, and regional prestige as primary justifications for public subsidies, empirical research consistently demonstrates that these facilities rarely produce significant, measurable net economic benefits for host cities.
The core economic argument against public financing centers on the substitution effect.
Economic models often fail to account for the fact that a large portion of spending at a stadium is not "new" money introduced into the local economy; rather, it is money that residents would have otherwise spent on other local entertainment options, such as restaurants, movie theaters, or other cultural activities.
Because this spending is simply redirected, there is little to no net increase in total local economic activity.
Furthermore, economic impact studies commissioned by proponents often rely on flawed multipliers that exaggerate the stimulative effect of sports expenditures.
These studies frequently ignore "leakage," where significant portions of the revenue (such as players' salaries) are exported out of the local economy because athletes and owners often do not reside in the city where they play.
Consequently, most independent academic research concludes that the public cost of these subsidies far exceeds any marginal economic growth they may stimulate.
Gary Kim was cited as a global "Power Mobile Influencer" by Forbes, ranked second in the world for coverage of the mobile business, and as a "top 10" telecom analyst. He is a member of Mensa, the international organization for people with IQs in the top two percent.
U.S. productivity has been rising for several years, but artificial intelligence is probably not the reason, at least, not yet.
According to a report published by the Federal Reserve Bank of San Francisco, the U.S. economy expanded at a relatively steady pace of around 2.5 percent per year over the past three years, even though employment growth slowed to near zero.
Almost by definition, higher output with the same input means higher productivity. But it is not clear artificial intelligence has much to do with the increases.
A survey of nearly 6,000 senior business executives in the United States, United Kingdom, Germany and Australia published by the National Bureau of Economic Research found:
69 percent of firms actively use AI
66 percent of executives regularly use AI
Average use is about 1.5 hours a week
90 percent of executives report little own-firm impact of AI over the last three years
90 percent report no impact on employment or productivity
Over the next three years, respondents predict that AI will boost productivity at their firms by an average of 1.4 percent
Will raise output 0.8 percent
Cut employment 0.7 percent
Employees believe AI will raise employment 0.5 percent in the next three years.
Perhaps the most-unexpected result is the employee belief that AI will actually boost employment at their firms over a three-year period. That findings seems at odds with the usual press reports suggesting employee angst about AI impact on employment.
The least-surprising result should be the inability to pinpoint AI productivity gains.
For starters, U.S. productivity has recently been rising since about 2019, well before AI emerged as a potential driver.
Labor productivity measures how efficiently workers use the capital available to them, such as equipment or software. The data suggests workers are doing so.
Total factor productivity uses a broader view, measuring how efficiently the economy uses all inputs together, including both labor and capital.
One interpretation might be that workers have been using tools effectively, but that the gains have not yet shown up in TFP metrics.
Think about your own work. Many of us would absolutely agree that AI has boosted our own personal productivity. But few of us can point to measurable gains in economic `outputs.
And U.S. productivity had been rising since about 1992 as well, to 2000.
For some observers, past experience suggests a productivity gain will happen. The U.S. economy has experienced several distinct productivity regimes over the past 70 years, including a high-growth period in the late 1990s, with the proliferation of computers and the internet, and a lengthy period of low average growth during the 2010s.
Right now, it appears there is a significant disconnect between labor productivity and TFP.
“The divergence between strong labor productivity growth and more modest TFP growth suggests that recent investments related to AI might be making workers more productive by providing them with better tools, such as new software and expanded computing capacity, but broader efficiency gains remain unrealized so far,” the report says.
But the report also says the pattern (Labor productivity and TFP misaligned) resembles what we saw when the internet became important.
There was a lag then, and there is arguably a lag now. As the adage goes, one can see the impact everywhere but in the outcomes (paraphrasing the Solow Paradox: "You can see the computer age everywhere but in the productivity statistics.").
Gary Kim was cited as a global "Power Mobile Influencer" by Forbes, ranked second in the world for coverage of the mobile business, and as a "top 10" telecom analyst. He is a member of Mensa, the international organization for people with IQs in the top two percent.
But space-based data centers are not an immediate replacement for terrestrial data center alternatives for reasons of initial cost and capacity. Launch costs remain substantial.
Potential upsides center on lower ongoing costs offsetting high upfront costs, eventually, though initial total operating costs will probably not match terrestrial alternatives:
Cheap/abundant power: Solar in orbit provides ~36% higher irradiance, near-constant supply (no night/clouds/weather), and very low marginal costs (projections ~$0.005/kWh vs. $0.04-0.08/kWh terrestrial wholesale). No grid connection, fuel, or large storage needed in ideal orbits.
Lower OpEx: Projections include 97% lower operating costs in some models (energy + cooling). No land, permitting, property taxes, or water for cooling. Avoids terrestrial delays/queues for power infrastructure.
Scalability and utilization: Unlimited "land" in orbit for expansion. High utilization from constant power. Falling launch costs could lead to cost parity or better for power-dominant workloads by late 2020s to 2030s.
Orbital systems could ease some important terrestrial obstacles:
Energy and emissions: Relies on clean solar (potentially 10x lower CO₂ emissions). Reduces strain on terrestrial grids, which often use fossil backups for data centers.
Resource Savings: No water consumption for evaporative cooling (a major terrestrial issue). Frees land for other uses; avoids local ecosystem/power price impacts from hyperscale farms.
Overall footprint: Could lower terrestrial data center growth, helping with power queues, water scarcity, and NIMBY opposition.
Of course, environmental impact is still there. Launch emissions, space debris (cluttering orbits, potential Kessler syndrome risk), manufacturing impacts and end-of-life disposal remain issues.
Some use cases might make more sense. Workloads tolerant of moderate latency (~100-500 ms round-trip) and benefiting from proximity to space data or constant power suggest suitability for:
AI Inference: Querying trained models (chat, search, voice agents, video generation, back-office automation)
Some telemetry use cases: Onboard near-source analysis of Earth observation, climate monitoring, disaster detection (wildfires/floods), maritime surveillance, sensor apps
Some edge compute cases: Real-time processing for satellites, space cybersecurity or autonomous operations or resilience against terrestrial outages/disasters
High-Security/ Sovereign Compute: Isolated environments for sensitive data, national security, or regions with poor terrestrial infrastructure.
Gary Kim was cited as a global "Power Mobile Influencer" by Forbes, ranked second in the world for coverage of the mobile business, and as a "top 10" telecom analyst. He is a member of Mensa, the international organization for people with IQs in the top two percent.
Among the many other changes artificial computing is raising for enterprise technologists and managers, AI also creates a new framework for thinking about older issues such as "cloud or on-prem?"
The new question is: "Which workloads justify dedicated GPU ownership, and which should be rented?"
Historically, the decision matrix was fairly simple.
But AI inference operations introduce new variables.
So the new decision-making matrix requires some understanding of when public cloud, private cloud or owned facilities provide the best economics for specific workloads.
For example, public cloud remains an optimal choice when utilization is uncertain or sporadic.
If GPU utilization is below roughly 30 percent to 40 percent, public cloud often is economically attractive.
But private cloud (enterprise-owned Infrastructure operated as a cloud) makes sense in other scenarios, such as trials or customer service operations, for reasons including customization, data control or security.
If workloads are predictable and GPU utilization exceeds roughly 50 percent to 60 percent, private infrastructure often becomes economically superior.
Owned facilities will make most sense for hyperscalers such as Amazon Web Services, Microsoft Azure, Google Cloud, large AI labs, major telecom operators, or very-large enterprises.
As often happens with computing technology, no single solution is right for every use case. For most large enterprises, the most-likely long-term architecture for large enterprises will often use public cloud, sometimes private cloud or owned facilities in some instances.
No solution will always be the best.
Pilots and training will normally be best suited for public cloud platforms. Proprietary models, regulated workloads or internal inference will be suited to private cloud.
Consumer-facing workload spikes are likely suited to use of public cloud, with high-volume inference likely an option for high-volume, sustained inference operations.
Gary Kim was cited as a global "Power Mobile Influencer" by Forbes, ranked second in the world for coverage of the mobile business, and as a "top 10" telecom analyst. He is a member of Mensa, the international organization for people with IQs in the top two percent.
SpaceX has completed the largest initial public offering in history, raising $75 billion. The listing priced 555.6 million shares at $135 ea...
